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##########    ACO algorithms for the TSP    ##########

      Version: 1.0
      Author:  Thomas Stuetzle
      Copyright (c) Thomas Stuetzle, 2002

This is the README file to the software package ACOTSP.

This software package was developed by Thomas Stuetzle in connection
with the Book 

[DorStu04] Marco Dorigo and Thomas Stuetzle, "Ant Colony
Optimization", MIT Press, Cambridge, MA, USA, 2004.

The software package is freely available subject to the 
GNU General Public Licence, which is included in file gpl.txt.

If you use ACOTSP in your research, I would appreciate a citation in
your publication(s). Please cite it as

Thomas Stuetzle. ACOTSP, Version 1.0. Available from, 2004.

This software package provides an implementation of various Ant Colony
Optimization (ACO) algorithms for the symmetric Traveling
Salesman Problem (TSP). The ACO algorithms implemented are Ant System,
Elitist Ant System, MAX-MIN Ant System, Rank-based version of Ant
System, Best-Worst Ant System, and Ant Colony System. This is Version
1.0 of ACOTSP; it is in large part identical to the software used to
produce the results in [DorStu04], but it has been slightly adapted to
make the code more readable, more comments were added, and a new
command line parser was generated with opag.

AIMS OF THE SOFTWARE: This software was developed to have one common
code for the various known ACO algorithms that were at some point
applied to the TSP in the literature. The software tries to provide a
reasonably efficient implementation of these ACO algorithms while at
the same time aiming for readability and understandability of the


The GNU General Public Licence:

The main control routines, main:

Procedures to implement the ants behaviour:

Input / output / statistics routines:

Procedures specific to the TSP:

Local search procedures:

Additional useful / helping procedure:

Command line parser:

Time measurement:
timer.c (you only need this one)
timer.h (and this one)
dos_timer.c (same as timer.c)
dos_timer.h (same as timer.c)
unix_timer.c (in case you want to use rusage instead, use this one)
unix_timer.h (in case you want to use rusage instead, use this one)


  Some problem instances from TSPLIB: eil51.tsp kroA100.tsp d198.tsp
  lin318.tsp pcb442.tsp att532.tsp rat783.tsp pcb1173.tsp d1291.tsp
  pr2392.tsp. Other TSP instances are available from TSPLIB, the
  webpage for the 8th DIMACS Implementation Challenge on the TSP
  ( or the webpage on "Solving


The software was developed in ANSI C under Linux, using the GNU 2.95.3
gcc compiler and extensively tested in this environment. The software
is distributed as a gzipped tar file.

To install the code, first obtain the file ACOTSP.V1.0.tar.gz. Unzip
the file by typing

gunzip ACOTSP.V1.0.tar.gz

and then unpack it by typing 

tar -xvf ACOTSP.V1.0.tar

The software will unpack in a new folder ACOTSP.V1.0 

To compile it under Linux just type 'make' and the executable 'acotsp'
is produced.

Note: The code is written in ANSI C. Hence, the code should be
reasonable portable to other Operating Systems than Linux or Unix.


Given the large number of ACO algorithms, also the number of command
line options is relatively large.

The default parameter settings are such, that MAX-MIN Ant System will
be run using a 3-opt local search, using alpha = 1, beta = 2, rho =
0.5 for a maximum of 10 seconds per each trial for 10 independent
trials. (guess who developed MAX-MIN Ant System ;-)

The executable 'acotsp' provides the following command line options
(given are the short and the long options):

-r, --tries          # number of independent trials
-s, --tours          # number of steps in each trial
-t, --time           # maximum time for each trial
-i, --tsplibfile     f inputfile (TSPLIB format necessary)
-o, --optimum        # stop if tour better or equal optimum is found
-m, --ants           # number of ants
-g, --nnants         # nearest neighbours in tour construction
-a, --alpha          # alpha (influence of pheromone trails)
-b, --beta           # beta (influence of heuristic information)
-e, --rho            # rho: pheromone trail evaporation
-q, --q0             # q_0: prob. of best choice in tour construction
-c, --elitistants    # number of elitist ants
-f, --rasranks       # number of ranks in rank-based Ant System
-k, --nnls           # No. of nearest neighbors for local search
-l, --localsearch    0: no local search   1: 2-opt   2: 2.5-opt   3: 3-opt
-d, --dlb            1 use don't look bits in local search
-u, --as               apply basic Ant System
-v, --eas              apply elitist Ant System
-w, --ras              apply rank-based version of Ant System
-x, --mmas             apply MAX-MIN ant system
-y, --bwas             apply best-worst ant system
-z, --acs              apply ant colony system
-h, --help             display the help text and exit

Options -u --as, -v --eas, -w --ras, -x --mmas, -y --bwas, -z --acs,
-h, --help don't need arguments, while all the others do.  

A Mandatory option is only the option "-i, --tsplibfile". Here, mandatory
means that without specifying this option, the program won't work,
since there is no input file. 

All the other options take some default values. The default values for
these are:

-r, --tries       : 10
-s, --tours       : 100
-t, --time        : 10 /* seconds */
-o, --optimum     : 1
-m, --ants        : 25
-g, --nnants      : 20
-a, --alpha       : 1
-b, --beta        : 2
-e, --rho         : 0.5
-q, --q0          : 0.0
-c, --elitistants : 100
-f, --rasranks    : 6
-k, --nnls        : 20
-l, --localsearch : 3 /* use 3-opt */
-d, --dlb         : 1 
-u, --as          : 0
-v, --eas         : 0
-w, --ras         : 0 
-x, --mmas        : 1 /* apply MAX-MIN Ant System */
-y, --bwas        : 0
-z, --acs         : 0

The default settings imply that as default MAX-MIN Ant System is run
using a 3-opt local search procedure. Please note that these default
values do not really make sense for some of the algorithms (e.g.,
typically an evaporation of 0.2 is recommended vor MAX-MIN Ant
System); that is, for some of the algorithms the default parameter
settings lead to poor performance (an example is ACS). Hence, when you
use any of the ACO algorithms, make sure you set the appropriate
parameter values. Typically, one may want to adjust the parameters

-t, --time
-o, --optimum
-m, --ants
-b, --beta
-e, --rho 
-q, --q0
-l, --localsearch

Note that only one option among -u --as, -v --eas, -w --ras,
-x --mmas, -y --bwas, -z --acs, is to be specified.

Examples for running an experiments are:

./acotsp -i lin318.tsp -v -t 60. -o 42029 -m 50 -b 5


./acotsp --tsplibfile lin318.tsp --acs --rho 0.1 --q0 0.95 --time 60. --optimum 42029 --ants 10


Every experiment produces three files. These files are 


where tsplibfilename is the instance identifier of the instance under

The most important of these is the file "cmp.tsplibfilename". This
file starts with a specification of the parameter settings used to run
the experiment. The section with the comprehensive experimental data
starts with

begin problem tsplibfilename

Next the random number seed for the next trial is given

Then, for each trial statistical information on the development of the
best-so-far solution is given. Each section for a trial starts with

begin try <trial_number>

Then, each time the algorithm finds a new best solution a line 

best <number>	 iteration <number>	 tours <number>	 time <number>

is added, where "best" is the tour length of the best-so-far solution;
iteration is the iteration number in which this solution is found;
tours is the number of solutions constructed so far (typically this is
simple iteration X n_ants); and time is the time at which a new
best-so-far solution is found

Each trial is ended by 

end try <trial_number>

Once all trials are run the line 

end problem tsplibfilename

is added to end the file. 

The file  best.tsplibfilename

collects the information about parameter settings, the best solution
found in each trial, and some additional statistical information.

The file stat.tsplibfilename 

may be used for the output of statistical information on a trial as
generated by the procedure population_statistics(); in InOut.c;
however, it is not heavily used in ACOTSP V1.0. 

Have fun, and if you have any comments please write to 

stuetzle no@spam


Ant Colony Optimization for the Travelling Salesman Problem



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